Judging Normality and Attractiveness in Faces: Direct Evidence of a More Refined Representation for Own-Race, Young Adult Faces
Bibliographic record
Abstract
Young and older adults are more sensitive to deviations from normality in young than older adult faces, suggesting that the dimensions of face space are optimized for young adult faces. Here, we extend these findings to own-race faces and provide converging evidence using an attractiveness rating task. In Experiment 1, Caucasian and Chinese adults were shown own- and other-race face pairs; one member was undistorted and the other had compressed or expanded features. Participants indicated which member of each pair was more normal (a task that requires referencing a norm) and which was more expanded (a task that simply requires discrimination). Participants showed an own-race advantage in the normality task but not the discrimination task. In Experiment 2, participants rated the facial attractiveness of own- and other-race faces (Experiment 2a) or young and older adult faces (Experiment 2b). Between-rater variability in ratings of individual faces was higher for other-race and older adult faces; reduced consensus in attractiveness judgments reflects a less refined face space. Collectively, these results provide direct evidence that the dimensions of face space are optimized for own-race and young adult faces, which may underlie face race- and age-based deficits in recognition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".